Event-driven Machine Learning Model Retraining Workflows

Question

Pulumi · Accepted Answer

Event-driven workflows for machine learning (ML) model retraining typically involve setting up a pipeline that automatically re-trains and possibly deploys a machine learning model when new data becomes available or certain conditions are met. This kind of workflow can be especially useful in scenarios where models need to adapt quickly to changes in data or where manual retraining is cumbersome and time-consuming.

To achieve an event-driven ML model retraining workflow on the cloud, you'll need several components:

Event Source: This could be new data arriving in a storage bucket, a change in the database, or a manual trigger from a user or external system.
Training Pipeline: This is a sequence of steps that processes the data, retrains the model, evaluates its performance, and decides whether to deploy the updated model. It may include data preprocessing, feature engineering, model training, testing, and potentially model deployment.
Model Serving Endpoint: Once the model is trained and deemed ready for use, it is often deployed to an endpoint where it can serve predictions.

Below is a Pulumi program in Python that outlines how you could set up an event-driven ML model retraining workflow using AWS services. It leverages AWS S3 for data storage, AWS Lambda for event handling, and AWS SageMaker for creating and running ML training jobs. This program is just a high-level template demonstrating how these resources can be defined using Pulumi.

Please note that machine learning and data processing require specific code tailored to the data and model you are using. The program below does not contain ML codes, such as data preprocessing or model definition. You would need to implement this within your AWS Lambda functions and SageMaker training jobs.

import pulumi
import pulumi_aws as aws

# Create an S3 bucket to store the training data and model artifacts.
data_bucket = aws.s3.Bucket('data-bucket')

# This lambda function will be invoked when new data is uploaded to the S3 bucket.
# It should contain the logic to start a new SageMaker training job with the new data.
retraining_lambda = aws.lambda_.Function('retraining-lambda',
    role=iam_for_lambda.arn,  # This IAM role should have permissions to access S3 and SageMaker
    runtime='python3.8',
    handler='handler.main',
    code=pulumi.AssetArchive({
        '.': pulumi.FileArchive('./retraining-lambda')  # Directory with your lambda function code
    })
)

# Grant the Lambda function necessary permissions.
retraining_policy = aws.iam.RolePolicy('retraining-policy',
    role=iam_for_lambda.name,
    policy=pulumi.Output.all(data_bucket.arn).apply(lambda arn: json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:StopTrainingJob",
                "sagemaker:CreateModel",
                "sagemaker:DeleteModel"
            ],
            "Resource": "*",
            "Effect": "Allow"
        },{
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                f"{arn}/*",  # Permission to access the data bucket
                arn
            ],
            "Effect": "Allow"
        }]
    }))
)

# Configure the S3 bucket to send an event to the Lambda function when new data is uploaded.
s3_event = aws.s3.BucketNotification("s3-event",
    bucket=data_bucket.id,
    lambda_functions=[aws.s3.BucketNotificationLambdaFunctionArgs(
        lambda_function_arn=retraining_lambda.arn,
        events=["s3:ObjectCreated:*"],
        filter_prefix="data/",
        filter_suffix=".csv",
    )],
    opts=pulumi.ResourceOptions(depends_on=[retraining_lambda]),
)

# Placeholder for a SageMaker training job definition.
# Replace '<training-job-definition-here>' with your training job configuration code.
# See AWS SageMaker documentation for details on configuring training jobs.
sagemaker_training_job_definition = '<training-job-definition-here>'

pulumi.export("bucket_name", data_bucket.bucket)
pulumi.export("lambda_arn", retraining_lambda.arn)

Explanation of the components:

S3 Bucket (data_bucket): This is where training data files are stored. When a new file is uploaded to the bucket, it can trigger an event.
Lambda Function (retraining_lambda): This function reacts to the event (new data uploaded) and starts a training job. The lambda function code, which is in the retraining-lambda directory, should dynamically generate a SageMaker training job configuration based on the new data.
IAM Role and Policy (retraining_policy): Permissions for the Lambda function to interact with SageMaker and S3.
S3 Bucket Notification (s3_event): Sends an event to the Lambda function when new data is uploaded. It is configured to listen for s3:ObjectCreated events specifically for CSV files in the data prefix.

To complete this program, you'll need to replace <training-job-definition-here> with the actual AWS SageMaker training job configuration code that aligns with your ML model and training requirements.

This Pulumi program should work "as is," but you will need to create the associated Lambda function handler (handler.main) and fill in the SageMaker training job configuration with relevant details for your specific ML workflow. The Lambda function is where the event handling logic is housed, including any preprocessing before triggering the retraining pipeline in SageMaker.